AU2021288876B2 - Hardness prediction method of heat hardened rail, thermal treatment method, hardness prediction device, thermal treatment device, manufacturing method, manufacturing facilities, and generating method of hardness prediction model - Google Patents
Hardness prediction method of heat hardened rail, thermal treatment method, hardness prediction device, thermal treatment device, manufacturing method, manufacturing facilities, and generating method of hardness prediction model Download PDFInfo
- Publication number
- AU2021288876B2 AU2021288876B2 AU2021288876A AU2021288876A AU2021288876B2 AU 2021288876 B2 AU2021288876 B2 AU 2021288876B2 AU 2021288876 A AU2021288876 A AU 2021288876A AU 2021288876 A AU2021288876 A AU 2021288876A AU 2021288876 B2 AU2021288876 B2 AU 2021288876B2
- Authority
- AU
- Australia
- Prior art keywords
- rail
- hardness
- cooling
- thermal treatment
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007669 thermal treatment Methods 0.000 title claims abstract description 71
- 238000000034 method Methods 0.000 title claims description 99
- 238000004519 manufacturing process Methods 0.000 title claims description 29
- 238000001816 cooling Methods 0.000 claims abstract description 512
- 238000009826 distribution Methods 0.000 claims abstract description 61
- 229910001566 austenite Inorganic materials 0.000 claims abstract description 36
- 238000010801 machine learning Methods 0.000 claims abstract description 17
- 238000004364 calculation method Methods 0.000 claims description 127
- 238000002347 injection Methods 0.000 claims description 61
- 239000007924 injection Substances 0.000 claims description 61
- 230000009466 transformation Effects 0.000 claims description 52
- 238000012546 transfer Methods 0.000 claims description 44
- 239000002826 coolant Substances 0.000 claims description 14
- 238000007637 random forest analysis Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 19
- 238000005096 rolling process Methods 0.000 description 19
- 238000004458 analytical method Methods 0.000 description 17
- 230000008859 change Effects 0.000 description 15
- 229910001562 pearlite Inorganic materials 0.000 description 14
- 239000000463 material Substances 0.000 description 12
- 230000008569 process Effects 0.000 description 12
- 239000000203 mixture Substances 0.000 description 10
- 230000020169 heat generation Effects 0.000 description 9
- 229910000831 Steel Inorganic materials 0.000 description 8
- 238000005259 measurement Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 8
- 239000010959 steel Substances 0.000 description 8
- 230000006399 behavior Effects 0.000 description 7
- 230000007423 decrease Effects 0.000 description 7
- 238000010438 heat treatment Methods 0.000 description 7
- 239000002344 surface layer Substances 0.000 description 7
- 238000005520 cutting process Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 6
- 239000000126 substance Substances 0.000 description 6
- 238000005098 hot rolling Methods 0.000 description 5
- 229910000734 martensite Inorganic materials 0.000 description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 241000446313 Lamella Species 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000000052 comparative effect Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000010583 slow cooling Methods 0.000 description 3
- 238000004781 supercooling Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 229910001563 bainite Inorganic materials 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 229910001567 cementite Inorganic materials 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 229910052742 iron Inorganic materials 0.000 description 2
- KSOKAHYVTMZFBJ-UHFFFAOYSA-N iron;methane Chemical compound C.[Fe].[Fe].[Fe] KSOKAHYVTMZFBJ-UHFFFAOYSA-N 0.000 description 2
- NRNCYVBFPDDJNE-UHFFFAOYSA-N pemoline Chemical compound O1C(N)=NC(=O)C1C1=CC=CC=C1 NRNCYVBFPDDJNE-UHFFFAOYSA-N 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000000452 restraining effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000007546 Brinell hardness test Methods 0.000 description 1
- 229910001035 Soft ferrite Inorganic materials 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 238000013145 classification model Methods 0.000 description 1
- 238000009749 continuous casting Methods 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 239000000498 cooling water Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000010410 layer Substances 0.000 description 1
- 238000007477 logistic regression Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000003595 mist Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000003303 reheating Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
- 229910000859 α-Fe Inorganic materials 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/40—Investigating hardness or rebound hardness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/40—Investigating hardness or rebound hardness
- G01N3/54—Performing tests at high or low temperatures
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D9/00—Heat treatment, e.g. annealing, hardening, quenching or tempering, adapted for particular articles; Furnaces therefor
- C21D9/04—Heat treatment, e.g. annealing, hardening, quenching or tempering, adapted for particular articles; Furnaces therefor for rails
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D1/00—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering
- C21D1/18—Hardening; Quenching with or without subsequent tempering
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D1/00—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering
- C21D1/62—Quenching devices
- C21D1/667—Quenching devices for spray quenching
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D11/00—Process control or regulation for heat treatments
- C21D11/005—Process control or regulation for heat treatments for cooling
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
- G01N25/18—Investigating or analyzing materials by the use of thermal means by investigating thermal conductivity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21D—MODIFYING THE PHYSICAL STRUCTURE OF FERROUS METALS; GENERAL DEVICES FOR HEAT TREATMENT OF FERROUS OR NON-FERROUS METALS OR ALLOYS; MAKING METAL MALLEABLE, e.g. BY DECARBURISATION OR TEMPERING
- C21D1/00—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering
- C21D1/56—General methods or devices for heat treatment, e.g. annealing, hardening, quenching or tempering characterised by the quenching agents
- C21D1/613—Gases; Liquefied or solidified normally gaseous material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0058—Kind of property studied
- G01N2203/0076—Hardness, compressibility or resistance to crushing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/0098—Tests specified by its name, e.g. Charpy, Brinnel, Mullen
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/0202—Control of the test
- G01N2203/0212—Theories, calculations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/0202—Control of the test
- G01N2203/0212—Theories, calculations
- G01N2203/0216—Finite elements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/022—Environment of the test
- G01N2203/0222—Temperature
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/40—Investigating hardness or rebound hardness
- G01N3/42—Investigating hardness or rebound hardness by performing impressions under a steady load by indentors, e.g. sphere, pyramid
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/20—Ensemble learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
Landscapes
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Thermal Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Mechanical Engineering (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Heat Treatment Of Articles (AREA)
- Control Of Heat Treatment Processes (AREA)
- Heat Treatments In General, Especially Conveying And Cooling (AREA)
- Metal Rolling (AREA)
- General Factory Administration (AREA)
Abstract
Thermal treatment of rails having a stable hardness distribution is enabled. The hardness of a rail, obtained by forced-cooling of a rail in the austenite region temperature or higher in a cooling facility (7) is predicted. A plurality of sets of learning data made up of a cooling conditions dataset and output data of hardness was acquired, using a model performing computation with the cooling conditions dataset having at least surface temperature of the rail before starting cooling and the operating conditions of the cooling facility (7) as input data, and the internal hardness of the rail after forced cooling as output data. A hardness prediction model is generated in advance by machine learning using the obtained plurality of sets of learning data, in which the cooling conditions dataset is input data at least, and information relating to internal hardness of the rail after forced cooling is output data. Hardness of the rail is predicted from the internal hardness of the rail as to one set of cooling conditions dataset set as cooling conditions of forced cooling, using the hardness prediction model.
Description
Title of Invention: HARDNESS PREDICTION METHOD OF HEAT
Technical Field
[0001]
The present invention is a technology relating to
manufacture of a heat hardened rail that includes a
thermal treatment process of executing forced cooling on
a rail having a high temperature equal to or higher than
an austenite region temperature. The present invention
is, in particular, a technology suitable for manufacture
of a heat hardened rail for a transportation railway,
which obtains a rail having at least a rail head portion
having excellent hardness uniformity by forcibly cooling
a rail heated to a temperature equal to or higher than
the austenite region temperature.
Background Art
[0002]
In the manufacture of a heat hardened rail, for the
purpose of improving quality such as hardness or
toughness, there is a case where forced cooling (thermal
treatment process) is executed on a rail manufactured by hot rolling. This forced cooling (thermal treatment process) is executed, for example, on a rail immediately after the end of rolling at a temperature equal to or higher than the austenite region temperature, or on a rail reheated to a temperature equal to or higher than the austenite region temperature after rolling and cooling. That is, the forced cooling is executed on the rail having a temperature equal to or higher than the austenite region temperature. In this specification, a rail that is manufactured via a thermal treatment process is also referred to as a heat hardened rail.
[00031
In particular, in a transportation railway that is
used at a natural resource mining site, a loading
capacity of a freight car is larger than that in a
general passenger railway. For this reason, in the
transportation railway, the rails are heavily worn, so
that the rails need to be replaced frequently. However,
rail replacement not only increases work costs or
replacement product cost, but also reduces a line
utilization rate. Therefore, there is a demand to reduce
the frequency of rail replacement. That is, in the
transportation railway, it is required to use rails with
high wear resistance.
[0004]
In order to obtain a rail with high wear resistance,
it is required that a hardness distribution in a region
inside the cross section from a rail surface to a predetermined depth (hereinafter, also simply referred to as "inside") is higher than a predetermined hardness value. Further, it is desirable that a crystal structure of the region is a pearlite structure. The reason is that a bainitic structure has low wear resistance even if it has the same hardness as the pearlite structure, and a martensite structure has low toughness.
[00051
In order to increase the hardness of the pearlite
structure, it is effective to make the spacing between
the ferrite and cementite layers (lamella) configuring
the microstructure fine. Then, in order to obtain a fine
lamella, it is necessary to cause transformation to
proceed in a super-cooled state where a rail having a
temperature equal to or higher than the austenite region
temperature is cooled at a high cooling rate (cold rate)
to a temperature sufficiently lower than an equilibrium
transformation temperature. However, in a case where the
cold rate is excessive, there is a concern that
transformation to the bainitic structure or the
martensite structure may occur and the characteristics
may deteriorate. Further, in general, the rail is cooled
mainly on the surface of a rail head portion, which is
the most important in terms of quality. At this time,
the rail head portion is a portion where the mass is most
concentrated, and therefore, in the rail head portion, a
large temperature difference tends to occur between the
surface and the inside during cooling. Therefore, in the rail head portion, a transformation start time also differs between the surface and the inside, so that it is necessary to control the microstructure inside the rail by controlling the cooling capacity according to the time difference.
[00061
As a technology for controlling the cooling of a
heat hardened rail, for example, there is a technology
described in PTL 1. PTL 1 discloses a method for
performing a first forced cooling, in which forced
cooling is performed from a temperature range of 7500C or
higher to a temperature in a range of 600 to 450°C at a
cooling rate in a range of 4 to 15°C/sec, and then
performing forced cooling again after pearlite
transformation is ended by temporarily stopping the
forced cooling.
[0007]
Further, PTL 2 discloses a method for changing the
conditions of forced cooling while determining a start
timing or an end timing of transformation heat generation
from the start of cooling, based on the temperature
measurement result of a rail surface.
[00081
Further, PTL 3 discloses a method for setting an
injection distance between a cooling nozzle and a rail
head portion, based on a carbon equivalent of a bloom
used as a rail material, by using hardness at a
representative point inside a rail set in advance and a relational expression between a carbon equivalent and an injection flow rate, an injection pressure, and an injection distance of a cooling medium, and setting a cooling time from the surface temperature (surface temperature before the start of cooling) of a head top portion of a rail measured on the inlet side of a cooling facility.
[00091
On the other hand, PTL 4 discloses a method for
predicting a temperature history and a microstructure
change inside a rail, and mechanical properties, and
setting cooling conditions for each cooling zone, based
on the prediction result, by using numerical, mechanical,
and metallurgical embedded models as a process model in a
control device.
Citation List
Patent Literature
[0010]
PTL 1: JP 4938158 B
PTL 2: JP 5686231 B
PTL 3: JP S61-149436 A
PTL 4: JP 6261570 B
Non Patent Literature
[0011]
NPL 1: Heat Transfer Engineering Material [Revised
5th Edition] Japan Society of Mechanical Engineers (2009)
[0012]
The method described in PTL 1 defines each condition
of the start and end of the first forced cooling and the
start and end of the second forced cooling, as the forced
cooling conditions of the heated rail. However, in a
case where the cooling conditions are set in advance by
the method described in PTL 1, if variation in the
component of the blooms of the rail material or variation
in cooling start temperature or the like occurs, there is
a problem in that variation occurs in the internal
hardness of the rail after thermal treatment.
[0013]
On the other hand, the method described in PTL 2 is
a method in which it is possible to consider the
influence of fluctuation factors such as material
variation or a cooling start temperature in that the
cooling conditions are changed based on the temperature
measurement result of the rail surface during the forced
cooling. However, in the method described in PTL 2,
there is a problem in that the cooling conditions are
changed only based on the surface temperature of the rail
head portion and the cooling conditions are not changed
to necessarily reflect a temperature change or a
microstructure change inside the rail. For example, a
temperature change due to heat conduction and a
temperature change due to transformation occur at the same time inside the rail, and a temperature at each position or a position where transformation occurs changes with the passage of time. Therefore, it is difficult to estimate a microstructure distribution of the inside only from the measurement result of the surface temperature.
[0014]
Further, in the method described in PTL 3, since the
hardness at a position 10 mm inside from the surface of
the rail head portion is predicted by a simple formula
using a physical model, it is possible to relate the
hardness inside the rail to the cooling conditions.
However, the microstructure formation inside the rail is
complicatedly affected by the heat transfer behavior on
the surface of the rail head portion by the cooling
medium, the heat conduction behavior inside the rail, the
microstructure change due to transformation, or the
transformation heat generation. Therefore, it is
difficult to accurately predict the hardness inside the
rail by the simple formula. Further, since the hardness
inside the rail differs according to the cross section,
it cannot be said that it is sufficient from the
viewpoint of securing the quality only by controlling the
hardness at a specific position.
[0015]
In the technology of PTL 4, it is described that the
final mechanical characteristics are predicted by
performing heat transfer analysis including transformation prediction online by using a chemical composition of rail steel, rolling conditions, an austenite grain size before cooling, expected transformation behavior, a geometric shape of a rail cross section, a temperature distribution, and target mechanical properties as input, and the cooling conditions are reviewed as necessary.
[0016]
However, since the cross-sectional shape of the rail
is complicated, two-dimensional or three-dimensional heat
conduction analysis or flow analysis of the cooling
medium is required for heat transfer analysis or
determination of the heat transfer coefficient, which is
a boundary condition, using a physical model. Therefore,
a calculation load is very large online. Furthermore,
heat transfer analysis including transformation is a non
linear phenomenon. For this reason, a stable solution
cannot be obtained unless a spatial resolution and time
step for analysis are made very small. Therefore, in the
capacity of the current computer, it is difficult to
appropriately correct the cooling conditions by ending
the calculation of heat transfer analysis during a period
from the end of hot rolling to the loading into the
cooling facility, determining appropriate cooling
conditions, and performing recalculation according to the
temperature history even in the cooling process.
[0017]
As described above, in the related art, there is a
problem in that when controlling a hardness distribution
inside the rail, it is realistically difficult to perform
accurate control according to a fluctuation factor such
as variation in the component of the bloom of the rail
material or a cooling start temperature. In particular,
the conditions of a material on the inlet side of the
cooling facility vary for each bloom. For this reason,
it is difficult to stably control the pearlite
transformation, which progresses quickly and generates a
large amount of heat, over the range from the surface to
the inside by processing using online heat transfer
analysis (numerical calculation by a physical model).
[0018]
The present invention has been made in view of the
points as described above, and seeks to provide a
technology for enabling thermal treatment of a rail
having a stable hardness distribution by controlling a
microstructure from the rail surface to the inside into a
desired microstructure.
Summary of the Invention
[0019]
In order to deal with the above limitations,
according to an aspect of the present invention, there is
provided a hardness prediction method for a heat hardened
rail, of predicting, after a thermal treatment process in
which a rail having a temperature equal to or higher than
an austenite region temperature is forcibly cooled in a cooling facility, hardness of the rail, the method including: acquiring, by using an internal hardness computing model that is a physical model for performing computing by using a cooling condition data set having at least a surface temperature of the rail before a start of cooling and operating conditions of the cooling facility for the forced cooling as input data and using hardness inside at least a rail head portion of the rail after the forced cooling as output data, a plurality of sets of data for learning composed of the cooling condition data set and the hardness output data; generating in advance a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the acquired plurality of sets of data for learning; and predicting the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process for achieving target hardness, obtained by using the hardness prediction model.
[0020]
Further, according to an aspect of the present
invention, there is provided a thermal treatment method
for a heat hardened rail having a thermal treatment
process in which a rail having a temperature equal to or
higher than an austenite region temperature is forcibly cooled in a cooling facility, the method including: measuring a surface temperature of the rail before a start of cooling; predicting hardness inside the rail by using the measured surface temperature of the rail by the hardness prediction method for a heat hardened rail according to an aspect of the present invention, before the start of cooling of the rail in the cooling facility; and resetting, in a case where the predicted hardness inside the rail is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range.
[0021]
Further, according to an aspect of the present
invention, there is provided a method for generating a
hardness prediction model for obtaining, after a rail
having a temperature equal to or higher than an austenite
region temperature is forcibly cooled in a cooling
facility, hardness of the rail from a cooling condition
data set for achieving target hardness having at least a
surface temperature of the rail before a start of cooling
in the cooling facility and operating conditions of the
cooling facility for the forced cooling, the method
including: acquiring, by using an internal hardness
computing model that is a physical model for performing
computing by using the cooling condition data set as
input data and using hardness inside at least a rail head portion of the rail after the forced cooling as output data, a plurality of sets of data for learning composed of the cooling condition data
- 11A - set and the hardness output data; and generating in advance a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the acquired plurality of sets of data for learning.
[0022]
Further, according to an aspect of the present
invention, there is provided a method for manufacturing a
heat hardened rail including: the thermal treatment
method for a heat hardened rail according to an aspect of
the present invention.
[0023]
Further, according to an aspect of the present
invention, there is provided a hardness prediction device
for a heat hardened rail, which predicts, after a thermal
treatment process in which a rail having a temperature
equal to or higher than an austenite region temperature
is forcibly cooled in a cooling facility, hardness of the
rail, the device including: a database configured to
store a plurality of sets of data for learning computed
using an internal hardness computing model that is a
physical model for performing computing by using a
cooling condition data set having at least a surface
temperature of the rail before a start of cooling and
operating conditions of the cooling facility for the
forced cooling as input data and using hardness inside at
least a rail head portion of the rail after the forced cooling as output data, and composed of the cooling condition data set and the hardness output data; a hardness prediction model generation unit configured to generate a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the plurality of sets of data for learning; a thermometer configured to measure the surface temperature of the rail before the start of cooling; and a hardness prediction unit configured to predict the hardness of the rail after the thermal treatment process, based on information on the hardness inside the rail with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process for achieving target hardness, by using a measured value measured by the thermometer and the hardness prediction model.
[0024]
Further, according to an aspect of the present
invention, there is provided a thermal treatment device
for a heat hardened rail having a thermal treatment
process in which a rail having a temperature equal to or
higher than an austenite region temperature is forcibly
cooled in a cooling facility, the device including: a
hardness prediction unit configured to predict hardness
inside the rail by the hardness prediction device for a
heat hardened rail according to an aspect of the present
invention, before a start of cooling of the rail in the cooling facility; and an operating condition resetting unit configured to reset, in a case where the hardness inside the rail predicted by the hardness prediction unit is out of a target hardness range, operating conditions of the cooling facility such that the predicted hardness inside the rail falls within the target hardness range.
[0025]
Further, according to an aspect of the present
invention, there is provided a manufacturing facility for
a heat hardened rail including: the thermal treatment
device for a heat hardened rail according to an aspect of
the present invention.
Here, as described above, the expression "inside the
rail" as used in this specification refers to a region
inside the cross section from the rail surface to a
predetermined depth.
[0026]
According to the aspect of the present invention,
the processing of computing the data (data for learning)
of the hardness distribution inside the rail after the
forced cooling for a plurality of cooling conditions,
which is processing with a large calculation load using
heat transfer analysis or the like, can be executed
offline, and therefore, it can be executed accurately.
Then, according to the aspect of the present invention,
the hardness prediction model for obtaining data of the
hardness distribution inside the rail after forced cooling with respect to the cooling conditions is obtained by machine learning, based on the accurate data for learning.
Therefore, according to the aspect of the present
invention, for example, it becomes possible to
appropriately control the microstructure of the region
from the head portion surface to the inside of the heat
hardened rail, and it becomes possible to manufacture the
heat hardened rail with the suppressed quality variation
by reducing variation in the hardness of each rail to be
manufactured or variation in hardness in the longitudinal
direction of the rail.
Brief Description of Drawings
[0027]
FIG. 1 is a schematic diagram illustrating a
manufacturing facility for a heat hardened rail according
to an embodiment based on the present invention.
FIG. 2 is a diagram describing disposition of a
header and the like for cooling in a cooling facility
according to the embodiment based on the present
invention.
FIG. 3 is a diagram describing a forced cooling
portion of a rail.
FIGS. 4A to 4C are diagrams illustrating examples of
a thermal treatment control method, in which FIG. 4A is a
diagram describing cooling conditions in a one-stage
cooling method, and FIGS. 4B and 4C are diagrams describing cooling conditions in a multi-stage step cooling method.
FIG. 5 is a diagram describing the relationship
between a surface temperature and a transformation
behavior by the one-stage cooling method.
FIG. 6 is a diagram describing the relationship
between a surface temperature and a transformation
behavior by the two-stage step method according to the
embodiment based on the present invention.
FIG. 7 is a diagram illustrating a configuration
example of a hardness prediction device.
FIG. 8 is a diagram illustrating a configuration of
an internal hardness offline calculation unit.
FIG. 9 is a diagram illustrating a configuration
example of a control device that perform hardness
control.
FIG. 10 is a diagram describing an example of a
target hardness setting method according to an embodiment
of the present invention.
FIGS. 11A to 11C are diagrams illustrating examples
in a case where the hardness of the rail is out of a
target range.
FIGS. 12A and 12B are diagrams describing other
examples of the target hardness setting method according
to an embodiment of the present invention.
Description of Embodiments
[0028]
Next, an embodiment of the present invention will be
described with reference to the drawings.
(Manufacturing facility 2 for heat hardened rail)
FIG. 1 is a schematic diagram illustrating an
example of a manufacturing facility 2 for a heat hardened
rail, which manufactures a heat hardened rail 1. The
manufacturing facility 2 illustrated in FIG. 1 includes a
heating furnace 11, a rolling machine 3, a cutting
machine 4, a cooling facility 7, and a cooling bed 10,
and these facilities are disposed in this order along a
transport direction (a pass line) for a rail material.
[0029]
<Heating furnace 11>
The heating furnace 11 executes treatment of heating
a bloom produced by a continuous casting facility or the
like so as to have a temperature equal to or higher than
an austenite region temperature on the inlet side of the
cooling facility 7, for example. However, this is not
the case having reheating treatment as a pre-process of
the cooling facility 7.
[0030]
<Rolling machine 3>
The rolling machine 3 is a hot rolling facility that
shapes and elongates the bloom heated in the heating
furnace 11 into a desired rail shape by a plurality of
rolling passes. The rolling machine 3 is usually
composed of a plurality of rolling stands.
[0031]
<Cutting machine 4>
The cutting machine 4 is a facility for dividing a
long rail 1 stretched by the rolling machine 3 in a
longitudinal direction, and is appropriately used
according to the length of the rail as a product and the
length of a rolled material. As the manufacturing
facility 2, for example, there is also a case where a
rail having a rolling length of about 100 m is
transported to the cooling facility 7 without being
divided, or a case where a rail is transported after the
length per piece is cut (sawn) into a length of, for
example, about 25 m.
[0032]
<Cooling facility 7>
The cooling facility 7 is a facility for performing
forced cooling (described later) on the rail 1 having a
high temperature equal to or higher than the austenite
region temperature. The cooling facility 7 is installed
along the pass line for the rail 1 in a manufacturing
line.
However, the cooling facility 7 does not need to
necessarily have a configuration in which it is installed
on the transport line from the rolling machine 3. For
example, a configuration is also acceptable in which the
cooling facility 7 is provided in an area different from
the hot rolling facility and the hot-rolled rail 1 is
reheated to a temperature equal to or higher than the
austenite region temperature in a heating furnace and then transported to the cooling facility 7. The cooling facility 7 is composed of a plurality of cooling zones disposed along the longitudinal direction of the rail 1 to be cooled, and the cooling zone to be used is set according to the length of the rail 1. The cooling conditions (operating conditions) of each cooling zone can be set individually.
Details of the cooling facility 7 will be described
later.
[00331
<Thermometer>
A thermometer 8 is provided at a position on the
inlet side of the cooling facility 7 (a position between
the cutting machine 4 and the cooling facility 7), and
detects the rail temperature before the start of cooling.
The measurement result measured by the thermometer 8 is
sent to a control device 6 that controls the cooling
facility 7. The thermometer 8 measures, for example, at
least the surface temperature of a head portion of the
rail 1.
Further, a thermometer 9 for detecting the
temperature of the surface of the rail 1 after the end of
forced cooling may be installed at a position on the
downstream side of the cooling facility 7 (the outlet
side of the cooling facility 7). In this case, the
validity of the prediction result of the control device 6
can be determined by comparing the temperature after the end of forced cooling predicted in the control device 6 with the temperature measured by the thermometer 9.
[0034]
<Cooling bed 10>
The rail 1 forcibly cooled in the cooling facility 7
is transported to the cooling bed 10.
The cooling bed 10 has, for example, a role of
correcting the rail 1 so as not to bend or a role of
uniformly cooling the rail 1. Further, in the cooling
bed 10, visual inspection, weight measurement, and the
like of the manufactured rail 1 are appropriately
executed.
[0035]
(Cooling facility 7)
The cooling facility 7 of the present embodiment is
configured to forcibly cool the head portion and foot
portion of the rail 1 carried to a treatment position by
a cooling medium that is injected from a cooling header.
The cooling header is provided for each cooling zone.
FIG. 2 is a diagram illustrating a disposition
example of the cooling header included in the cooling
facility 7 by a schematic diagram as viewed from a rail
cross section. That is, as illustrated in FIG. 2, the
cooling header of the present embodiment includes a head
top cooling header 71 and a head side cooling header 72
for cooling a head portion 101 of the rail 1, and a foot
underside cooling header 73 for cooling a foot portion
103 of the rail 1. If necessary, a web portion cooling header for cooling a web portion 102 of the rail 1 may be further provided. Further, the head top cooling header
71 and the head side cooling header 72 are collectively
referred to as a "head portion cooling header".
[00361
Each of the head top cooling header 71, the head
side cooling header 72, and the foot underside cooling
header 73 (hereinafter, they are collectively referred to
as "cooling headers 71, 72, and 73" as appropriate) is
connected to a cooling medium source through a pipe, and
the cooling medium is injected from a plurality of
nozzles (not illustrated. Further, the pipe is provided
with a control valve.
Here, a cooling method which the cooling facility 7
of the present embodiment adopts is air impinging
cooling. The air impinging cooling is a method for
injecting compressed air as the cooling medium, which can
achieve a cooling rate suitable for the present invention
and has little fluctuation in cooling capacity with
respect to the surface temperature of a material to be
cooled. However, the cooling method in the present
embodiment is not limited to the air impinging cooling,
and may be a water cooling method including mist cooling.
[0037]
The specific nozzle disposition of each cooling
header is as follows. That is, the nozzle of the cooling
header 71 is disposed above the head portion 101 of the
rail 1 along the longitudinal direction of the rail 1.
The nozzle of the cooling header 71 injects the cooling
medium (air) toward an upper surface (head top surface)
1011 of the head portion 101 illustrated in FIG. 3.
Further, the nozzles of the cooling headers 72 are
disposed along the longitudinal direction of the rail 1
on both sides of the head portion 101 of the rail 1 at
the treatment position. The nozzle of the cooling header
72 injects the cooling medium (air) toward a side surface
(head side surface) 1012 of the head portion 101
illustrated in FIG. 3. Further, the nozzle of the foot
underside cooling header 73 is disposed along the
longitudinal direction of the rail 1 below the foot
portion 103 of the rail 1 at the treatment position. The
nozzle of the foot underside cooling header 73 injects
the cooling medium (air) toward an underside surface
(foot underside surface) 1031 of the foot portion 103
illustrated in FIG. 3.
[00381
As the type of the nozzle, a group jet composed of a
plurality of circular tube nozzles, a slit nozzle
composed of slits having a rectangular gap, or the like
is suitable. In the air impinging cooling, it is
generally known that the cooling capacity (heat transfer
coefficient) can be controlled by adjusting an injection
pressure and an injection distance (for example, NPL 1).
Therefore, each of the cooling headers 71, 72, and 73 has
a configuration in which pressure can be controlled in
order to control the injection of the cooling medium
(air). Further, for the purpose of matching a difference
in the cross-sectional shape of the rail 1 according to
the standard of the rail 1 and the purpose of controlling
the cooling capacity, the cooling facility 7 is provided
with a moving mechanism for each cooling header, whose
distance from the surface of the rail 1 can be adjusted.
As a position adjusting mechanism of each of these
headers, there is an electric actuator, an air cylinder,
a hydraulic cylinder, or the like. As the position
adjusting mechanism of the present embodiment, the
electric actuator is suitable from the viewpoint of
positioning accuracy. Further, a range finder (for
example, a laser displacement meter) (not illustrated)
for measuring the distance from the surface of the rail 1
to each cooling header is provided. Then, the injection
distance of each cooling header during cooling can be
controlled according to a setting value. In addition, in
order to prevent the distance from the header from being
changed due to the deformation of the rail 1 due to
thermal contraction during cooling, there is provided a
restraining device (not illustrated) that clamps the foot
portion 103 or the like of the rail 1 and restrains the
deformation in the up-down and right-left directions.
[00391
Further, as illustrated in FIG. 2, the cooling
facility 7 includes a head portion thermometer 74 and a
foot portion thermometer 75. The head portion
thermometer 74 is provided above the head portion 101 of the rail 1 and measures the surface temperature of the head portion 101 (for example, one location in the head top surface 1011). The foot portion thermometer 75 is provided below the foot portion 103 of the rail 1 and measures the surface temperature of the foot portion 103
(for example, one location in the foot underside surface
1031). As each of these two types of thermometers 74 and
75, a plurality of thermometers are installed in the
longitudinal direction within the cooling facility 7, and
the temperature history of each place during cooling can
be monitored by these two types of thermometers 74 and
75. Further, there is a case where thermometers (not
illustrated) for monitoring the temperature of the air
(cooling medium) that is injected are installed at a
plurality of headers. This is because the injection
temperature also affects the cooling capacity.
Here, the injection pressure, the injection
distance, the injection position, the injection time, and
the like of the cooling medium that is injected toward
the rail 1 in the cooling facility 7 are controlled by
the control device 6, so that the cooling conditions can
be adjusted.
[0040]
<Thermal treatment method>
Next, the principle and others of the forced cooling
treatment (thermal treatment) by the cooling facility 7
will be described.
Here, it is assumed that the rail 1 before the
forced cooling has been heated to a temperature equal to
or higher than the austenite region temperature. Then,
in the cooling facility 7, the forced cooling is executed
on the high temperature rail 1, based on the cooling
conditions. Due to this forced cooling, a temperature
change or transformation in the surface and inside of the
rail 1 proceeds, and a microstructure inside the rail 1
after the thermal treatment can be controlled by changing
the cooling conditions by the head portion cooling header
at any time.
[0041]
As a method for controlling the thermal treatment
(forced cooling), there is a multi-stage step method such
as a one-stage cooling method (FIG. 4A), a two-stage step
method (FIG. 4B), a three-stage step method (FIG. 4C), as
illustrated in FIGS. 4A to 4C. The one-stage cooling
method is a method in which cooling is performed under a
condition in which the injection flow rate, pressure, and
injection distance of the cooling header as the cooling
conditions are constant from the start of cooling to the
end of cooling. The multi-stage step method is a method
in which the cooling conditions are set to two stages (a
front stage and a subsequent stage) or three or more
stages from the start of cooling and the cooling
conditions are changed stepwise with the passage of time.
In the present embodiment, the multi-stage step method is
adopted.
In the case of the multi-stage step method, in each
step, the injection flow rate, pressure, and injection
distance of the cooling header are determined and a
timing for transition to the next step is determined.
However, the change of the cooling conditions does not
always need to adopt the multi-stage step method in
response to the passage of time, and the cooling
conditions may be set as a function of time such that the
cooling conditions to be changed can be specified with
the passage of time.
[0042]
The cooling conditions can be set individually for
each cooling zone divided in the longitudinal direction.
Further, in the head side cooling headers 72, the cooling
conditions of the left and right cooling headers may be
set to different conditions. Further, the injection flow
rate, pressure, and injection distance of the cooling
header may be changed in a stepped manner individually or
in combination of two or more conditions. However, in a
case where the change is performed by the combination of
two or more conditions, a plurality of conditions are
changed at the same time according to the time step in
FIGS. 4A to 4C.
Here, in eutectoid steel that is widely used as the
material of the rail 1, the transformation from austenite
to pearlite occurs in a temperature range of about 550°C
to 7300C. In practice, in order to achieve both
suppression of bainite and high hardness, it is desirable that the transformation occurs in the temperature range of 570 to 590°C.
[00431
In the case of the transformation in such a target
temperature range using the two-stage step method, the
cooling in the front stage step is, for example, cooling
from the start of cooling to before the surface starts
transformation, and the cooling rate in the front stage
step is preferably set to the range of 4 to 6°C/sec. In
a case where the cooling rate is slower than this range,
the transformation occurs at a high temperature and the
hardness decreases. Further, in a case where the cooling
rate is faster than this range, there is a concern that
bainite transformation may occur.
[0044]
FIG. 5 schematically illustrates an example of a
microstructure change of the surface layer of the head
portion of the rail 1. FIG. 5 illustrates an example in
which the one-stage cooling method in which the cooling
conditions are kept constant from the start to the end of
the forced cooling is applied as the method for
controlling thermal treatment. As illustrated in FIG. 5,
in a case where cooling is continued at a constant
injection pressure, if pearlite transformation occurs, a
rapid temperature rise AT (in a range of 80 to 120°C)
occurs due to transformation heat generation. Since such
a temperature rise softens the pearlite structure of the
surface layer, it does not become possible to secure a predetermined hardness. Further, due to the influence of the transformation heat generation of the surface layer, the cooling rate decreases at a position about 5 to 10 mm inside from the surface, and a super cooling degree decreases, so that the hardness inside the rail 1 after the thermal treatment also decreases. That is, when the thermal treatment control method is the one-stage cooling method, there is a concern that the hardness inside the rail 1 after the thermal treatment may not reach a target hardness.
[0045]
In contrast, in the two-stage step method, as
illustrated in FIG. 6, the cooling capacity can be
increased in accordance with the transformation heat
generation in the subsequent stage step after the
transformation heat generation of the surface is started.
By strengthening the cooling at a timing when the
transformation starts on the surface in this manner, the
temperature rise due to the transformation heat
generation can be suppressed, and the pearlite structure
of the surface is not easily softened. Further, it is
possible to suppress a decrease in the cooling rate
inside the rail 1 due to the influence of the
transformation heat generation on the surface. However,
if the cooling rate in the subsequent stage step is too
high, the surface is strongly cooled while the pearlite
transformation of the surface is not completed, and there
is also a case where some bainitic structures may be generated. Therefore, in the subsequent stage step, it is desirable that the average cooling rate from the start of transformation heat generation on the surface to the end of cooling is in the range of 1 to 2°C/sec.
[0046]
When performing such two-step cooling (two-stage
step method) by pressure control in the air impinging
cooling, it is favorable if low-pressure air is injected
in the slow cooling of the front stage step and high
pressure air is injected in the cooling of the subsequent
stage step in which a temperature rise due to the
transformation heat generation becomes remarkable.
Pressure adjustment is generally performed using a flow
rate regulation valve. Further, when performing the two
step cooling by changing the injection distance, in the
slow cooling of the front stage step, air is injected
from a long distance, and in the subsequent stage step
cooling in which the influence of the transformation heat
generation is large, air is injected from a close
distance, so that a similar effect can be obtained.
[0047]
Incidentally, the transformation start time (a time
when the cooling curve intersects a pearlite
transformation start curve P) illustrated in FIG. 5 or
FIG. 6 changes according to the cooling start
temperature. Further, if the shape of the rail 1
changes, the mass of the head portion changes.
Therefore, even if the same cooling conditions are set, the required cooling rate or cooling capacity changes.
Therefore, it is necessary to control not only the timing
of switching the cooling conditions from slow cooling to
strong cooling by the control device 6 but also the
cooling capacity by the injection pressure or the
injection distance in each step. Further, since the
transformation curve illustrated in FIGS. 5 and 6 changes
according to the chemical composition of the rail steel
or the austenite grain size before cooling, there is also
a case where the cooling conditions are changed according
to the components contained in the bloom of the rail
material or the pass schedule in the rolling machine 3,
which affects the austenite grain size before cooling.
[0048]
(Hardness prediction method for heat hardened rail 1
(hardness prediction device 20))
The present embodiment has a hardness prediction
device 20. The hardness prediction device 20 is a device
for realizing a hardness prediction method for the heat
hardened rail 1, which predicts the hardness of the rail
1 after the thermal treatment process in which the forced
cooling is performed on the rail 1 having a temperature
equal to or higher than the austenite region temperature
in the cooling facility 7.
As illustrated in FIG. 7, the hardness prediction
device 20 includes a basic data acquisition unit 21, a
database 23 (a storage unit), a hardness prediction model
generation unit 24, and a hardness prediction unit 26.
The hardness prediction unit 26 is used online and is
incorporated into the control device 6.
Here, the set of data of cooling conditions having
at least the surface temperature of the rail 1 before the
start of cooling in the cooling facility 7 and the
operating conditions of the cooling facility 7 is
referred to as a cooling condition data set.
[0049]
The cooling condition data set that is used offline
includes numerical information corresponding to
temperature information that is acquired by the
thermometer 8 disposed on the inlet side of the cooling
facility 7 as the surface temperature of the rail 1
before the start of cooling. Further, as the operating
conditions of the cooling facility 7, the injection flow
rate, the injection pressure, and the injection distance
of each cooling header in each step from the start of
cooling to the end of cooling and a switching timing of
the cooling step (for example, a time from the start of
cooling to the switching of each step) are included.
The cooling condition data set may include input
information of the thermal treatment for cooling other
than the surface temperature of the rail 1 before the
start of cooling and the operating conditions of the
cooling facility 7.
[0050]
<Basic data acquisition unit 21>
The basic data acquisition unit 21 has an internal
hardness computing model which is a physical model for
performing computing by using an offline cooling
condition data set as input data and using the hardness
inside at least the rail head portion of the rail 1 after
the forced cooling as output data. In the present
embodiment, the execution of the numerical calculation
using the internal hardness computing model is performed
in the internal hardness offline calculation unit 22.
Then, the basic data acquisition unit 21 acquires a
plurality of sets of data for learning composed of a
cooling condition data set as input data and the hardness
information inside the rail 1 as output data by executing
offline computing by the internal hardness offline
calculation unit 22 individually with respect to the
plurality of cooling condition data sets. The basic data
acquisition unit 21 stores the acquired data for learning
in the database 23.
[0051]
In the present embodiment, the data of the internal
hardness, which is the output data computed by the
internal hardness offline calculation unit 22, is
expressed by an internal hardness distribution in at
least a region from the surface of the rail 1 to a depth
set in advance. The depth set in advance is, for
example, 10 mm or more and 50 mm or less. The depth set
in advance is set to, for example, a value equal to or
larger than a limit value of a wear depth that can withstand practical use even if the surface layer of the head portion of the rail 1 is worn. Conventionally, it is preferably set to 1 inch (25.4 mm).
[0052]
That is, the basic data acquisition unit 21 of the
present embodiment has an internal hardness offline
calculation unit 22 that is executed offline and executes
numerical calculation using a set of cooling condition
data sets composed of at least the surface temperature
before the start of cooling and the operating conditions
of the cooling facility 7 as input data and using the
hardness distribution inside the rail 1 after the thermal
treatment process as output data, and has a function of
changing the cooling condition data set in various ways,
calculating the hardness distribution inside the rail 1
for each cooling condition data set, and sending data for
learning indicating the relationship between the obtained
cooling condition data set and the hardness distribution
to the database 23.
[0053]
Here, configuration data such as a plurality of
cooling condition data sets or data such as the surface
temperature before the start of cooling configuring the
cooling condition data set may be stored in the database
23 in advance. For each cooling condition data set, for
example, a range of a temperature condition or the like
is set based on past operating conditions, conditions of
the rail 1 to be manufactured in the future, or the like, and the cooling condition data set is determined from the values within the set range. However, the plurality of cooling condition data sets to be used do not need to be necessarily stored in advance in the database 23, and may be configured to be directly input to the internal hardness offline calculation unit 22.
[0054]
<Internal hardness offline calculation unit 22
(internal hardness computing model)>
As illustrated in FIG. 8, the internal hardness
offline calculation unit 22 includes a heat transfer
coefficient calculation unit 22A, a heat conduction
calculation unit 22B, a microstructure calculation unit
22C, and a hardness calculation unit 22D. In the
hardness calculation based on the physical model by the
internal hardness offline calculation unit 22, the
calculation from the start of cooling to the end of
cooling is performed in the order of heat transfer
coefficient calculation, heat conduction calculation, and
the processing of the microstructure calculation unit
22C, and can be obtained by calculating the hardness from
the result of the final microstructure calculation.
[0055]
However, in a case of performing coupled analysis as
described later, when performing calculation from the
start of cooling to the end of cooling, for example, at
every time step in a range of 0.1 to 10 ps, a coupled
calculation is performed between the heat transfer coefficient calculation, the heat conduction calculation, and the processing of the microstructure calculation unit
22C. After those calculations are ended, the
calculations is executed with respect to the next time
step. A method for repeating the above processing until
the end of cooling is adopted. Since the hardness
calculation is not incorporated into the coupled
analysis, it is favorable if the calculation is performed
based on the microstructure calculation result after the
end of cooling.
The location of the rail 1 to be computed does not
need to be necessarily executed on the entire surface of
the rail 1. The internal hardness offline calculation
unit 22 of the present embodiment is used in a case of
computing at least the hardness of the head portion of
the rail 1 where uniform hardness is most required.
Further, a known model formula may be adopted as the
calculation formula of the internal hardness offline
calculation unit 22 for obtaining the hardness
distribution from the cooling condition data set.
[00561
<Heat Transfer Coefficient Calculation Unit 22A>
The heat transfer coefficient calculation unit 22A
calculates the heat transfer coefficient on the surface
of the rail 1 during the thermal treatment. The heat
transfer coefficient calculation unit 22A of the present
embodiment computes the heat transfer coefficients at a plurality of locations on the surface of the head portion of the rail 1.
The heat transfer coefficient calculation unit 22A
of the present embodiment calculates the heat transfer
coefficient of the surface of the rail 1 by a numerical
fluid dynamics method such as a finite volume method by
inputting the operating parameters of the cooling
facility 7 and the rail shape. The finite volume method
is a method in which a region to be analyzed is divided
into a finite number of control volumes and an integral
type physical quantity conservation equation is applied
to each volume. However, the heat transfer coefficient
may be calculated by an experimental formula relating to
forced convection, in which the relationship between
dimensionless quantities such as the Nusselt number or
the Reynolds number is obtained from a cooling
experiment.
[0057]
At this time, in the heat transfer coefficient
calculation unit 22A, a time-series heat transfer
coefficient (distribution of heat transfer coefficient
that changes with time) at each position of the surface
of the head portion of the rail 1 is obtained according
to the injection flow rate, the injection pressure, and
the injection distance, and the switching timing of the
cooling step of each cooling header in each step from the
start of cooling to the end of cooling. Further, the temperature of the injected cooling medium may be included in the variable.
[00581
<Heat conduction calculation unit 22B>
The heat conduction calculation unit 22B calculates
heat conduction inside the rail 1 by thermal treatment,
for example, heat conduction in a two-dimensional cross
section of the rail 1, by using the heat transfer
coefficient calculated by the heat transfer coefficient
calculation unit 22A as a boundary condition. As the
heat conduction calculation, for example, the temperature
distribution in the cross section is obtained.
The heat conduction calculation unit 22B of the
present embodiment calculates a temperature history (heat
conduction calculation) inside the rail 1 from the start
of cooling to the end of cooling by using the heat
transfer coefficient at each position on the surface of
the head portion of the rail 1 output by the heat
transfer coefficient calculation unit 22A as a boundary
condition and using a numerical heat transfer analysis
method such as a finite element method. Further, values
such as thermal conductivity, specific heat, and density
as physical property values required for heat conduction
calculation are appropriately changed according to the
component composition of the target rail 1.
[00591
In the two calculation units 22A and 22B described
above, sufficient calculation accuracy can be obtained even in a method for performing the calculation of the heat conduction calculation unit 22B for calculating a temperature field by using the calculation result by the heat transfer coefficient calculation unit 22A for calculating a flow field. However, in a case where it is desired to further improve the calculation accuracy, coupled analysis may be performed in consideration of the interaction between the flow field and the temperature field. Although the calculation accuracy is improved in the coupled analysis, it is practically difficult to apply it in an online analysis because the calculation load increases. However, in the present invention, the load increase is allowed because these analyses are executed offline.
[00601
<Microstructure calculation unit 22C>
The microstructure calculation unit 22C performs
microstructure prediction in the cross section of the
rail 1 considering phase transformation, from the
temperature distribution inside the rail 1 based on the
temperature history calculation calculated by the heat
conduction calculation unit 22B. The microstructure
prediction in the cross section is, for example, a
microstructure distribution in the cross section.
[0061]
The microstructure calculation unit 22C of the
present embodiment performs microstructure prediction at
each position in the cross section of the rail 1 in consideration of the phase transformation, from the temperature history inside the rail 1 obtained by the heat conduction calculation unit 22B. Since the behavior of the phase transformation changes according to the component composition of steel to be thermally treated or the austenite grain size before the start of cooling, the calculation is performed for each component composition corresponding to the standard of the target rail 1.
Further, the austenite grain size changes according to
the pass schedule in the rolling machine 3 or the time
required from the end of rolling to the start of forced
cooling. Therefore, the microstructure calculation may
be performed for each of these operating conditions, and
a microstructure prediction model for predicting the
austenite grain size before the start of forced cooling
may be further added. In the offline calculation, even
in a case where the component composition of steel that
is a rail material or the austenite grain size is
different, it is possible to perform the calculation with
respect to a large number of conditions in advance.
Therefore, these parameters may be added as input data of
the hardness prediction model 25 (described later).
[0062]
Further, in the microstructure calculation unit 22C
of the present embodiment, a phase transformation
calculation incorporating dynamic phase transformation
characteristics such as a change in the phase
transformation start temperature or a change in the progress rate of the phase transformation according to the cooling rate is performed.
Here, since not only the temperature history affects
the phase transformation but also the temperature history
is affected by the transformation heat generation, it is
desirable that the microstructure calculation unit 22C
and the heat conduction calculation unit 22B described
above perform the coupled analysis. For the calculation
of the transformation behavior in the microstructure
calculation unit 22C, for example, a known calculation
formula described in a method by Ito et al. (Iron and
steel, 64 (11), S806, 1978, or Iron and steel, 65 (8),
A185-A188, 1979) or the like can be used.
[00631
<Hardness calculation unit 22D>
The hardness calculation unit 22D calculates the
hardness distribution in the cross section of the rail 1
from the microstructure distribution based on the
microstructure prediction of each cross section
calculated by the microstructure calculation unit 22C.
In the hardness calculation unit 22D of the present
embodiment, the predicted hardness is calculated using a
relational expression between each microstructure and the
hardness with a chemical composition or the degree of
super-cooling as input. For example, a pearlite
structure is a lamella microstructure in which plate-like
soft ferrite and hard cementite are layered, and it is
known that there is a strong correlation between lamella spacing and hardness, and for example, a method by A. R.
Marder et al. (The Effect of Morphology on the Strength
of Pearlite: Met. Trans. A, 7A (1976), 365-372) can be
used. Further, as the relational expression between the
chemical composition, the degree of super-cooling, and
the hardness of each microstructure, an experimental
formula obtained in advance by an experiment or the like
may be used.
[0064]
<Database 23>
A data set in which the surface temperature of the
rail 1 before the start of cooling, and the injection
flow rate, the injection pressure, the injection
distance, and the switching timing of the cooling step of
each cooling header from the start of cooling to the end
of cooling as the operating conditions of the cooling
facility 7 are variously changed is generated as the
cooling condition data set by using the internal hardness
offline calculation unit 22. Further, the result of
calculating the hardness distribution inside the rail 1
corresponding to each data set is stored in the database
23 as data for learning.
Here, the hardness distribution inside the rail 1
which is the calculation result is expressed by hardness
data corresponding to each position (the coordinates in
the cross section) in the cross section of the head
portion 101 of the rail 1. However, the hardness data of
the hardness distribution is not a continuous value, but a discrete value according to the element division used in the calculation of the heat conduction calculation unit 22B or the microstructure calculation unit 22C.
[00651
Further, since there is little practical need for a
fine hardness distribution in the cross section, it is
sufficient if the hardness data extracted at a pitch in a
range of about 1 to 5 mm as the coordinates in the cross
section is used as the hardness distribution. As for the
hardness data, the calculation results may be averaged
for each pitch. Further, all the hardness information in
the cross section is not necessary, and for example, data
of the position and hardness in the vertical direction
from the head top surface 1011 may be used as the
hardness distribution inside the rail 1. Further, in
addition to this data, as specified in JIS E 1120-2007,
the position and hardness data at a position diagonally
advanced from a head corner portion (the boundary portion
between 1011 and 1012) may be used. At that time, as
representative positions in an inward direction from the
surface, several representative points such as depths of
2, 5, 10, 15, 20, and 25 mm are used, and the
corresponding hardness data can be used as the hardness
distribution inside the rail 1.
[00661
On the other hand, a diagram indicating the hardness
distribution in the cross section of the rail 1 with
contour lines or color-coded image data (data expressing the hardness distribution as an image) may be defined as the hardness distribution inside the rail 1. This is because, in machine learning means such as deep learning, generation of a hardness prediction model 25 using an image as output data is possible.
The cooling condition data set, which is the input
data when constructing the database 23, may change the
cooling condition within the range with reference to the
past operating results. Further, within the range of the
facility specifications of each cooling header of the
cooling facility 7, the input conditions for the
calculation are appropriately changed, and the
calculation is performed by the internal hardness offline
calculation unit 22.
[0067]
As described above, the combination of a plurality
of sets of input data (cooling condition data set) and
output data (hardness calculation result) is created and
stored in the database 23 in advance.
The data for learning to be stored may be a set of
500 or more input data (cooling condition data set) and
output data (hardness calculation result). Preferably,
2000 or more data for learning are generated.
[0068]
<Hardness prediction model generation unit 24>
In the hardness prediction model generation unit 24,
the hardness prediction model 25 using the cooling
condition data set as at least input data and using information on the hardness inside the rail 1 after forced cooling as output data is generated by the machine learning using a plurality of sets of data for learning stored in the database 23. The generation of the hardness prediction model 25 is executed offline.
A machine learning model to be used may be any model
as long as the hardness can be predicted with the
accuracy necessary for practical use. As the machine
learning model, for example, a commonly used neural
network (including deep learning), decision tree
learning, random forest, support vector regression, or
the like may be used. Further, an ensemble model in
which a plurality of models are combined may be used.
[00691
Further, as the hardness prediction model 25, a
machine learning model which determines whether or not
the hardness value of the rail 1 is within the allowable
range of the hardness distribution determined in advance,
and uses data, in which the result is binarized as
pass/fail, as output data may be used. At that time, it
is preferable to use a classification model such as a k
nearest neighbor method or logistic regression.
[0070]
<Control device 6>
As illustrated in FIG. 1, the manufacturing facility
2 for the rail 1 of the present embodiment includes the
control device 6 for controlling the cooling conditions
of the rail 1.
The control device 6 acquires the shape, chemical
composition, target hardness (internal distribution), and
reference cooling conditions of the rail 1 from a host
computer 5, calculates the operating conditions for
realizing them, issues a command to the cooling control
device, and determines the operating parameters of the
cooling facility 7.
The configuration of the control device 6 in the
present embodiment is illustrated in FIG. 9.
As illustrated in FIG. 9, the control device 6
includes an operating condition initial setting unit 61,
the hardness prediction unit 26, an operating condition
determination unit 62, and an operating condition
resetting unit 63 of the cooling facility 7.
[0071]
<Operating condition initial setting unit 61>
The operating condition initial setting unit 61 sets
the injection pressure or the injection distance, and the
injection position of the cooling header, and the
switching timing of them in advance so as not to generate
an abnormal microstructure such as the bainitic structure
or the martensite structure while satisfying the target
hardness distribution. These cooling conditions can be
determined offline by an empirical rule based on the past
operating results, methods described in PTLs 1 to 3, or
the like. Further, appropriate cooling conditions for
obtaining the target hardness are determined in advance
with respect to the representative values of the rail type, standard, dimensions, and chemical composition of the rail 1 by using the basic data acquisition unit 21, and these conditions may be set in the operating condition initial setting unit 61 of the cooling facility
7.
[0072]
<Hardness prediction unit 26>
The hardness prediction unit 26 predicts the
hardness of the rail 1 after the thermal treatment
process, based on the hardness inside the rail 1 with
respect to a set of cooling condition data sets that are
set as cooling conditions of the thermal treatment
process, which is obtained by using the hardness
prediction model 25.
The hardness prediction unit 26 of the present
embodiment configures the cooling condition data set by
using the surface temperature of the head portion of the
rail 1 measured by the thermometer 8 on the inlet side of
the cooling facility 7 and the cooling condition of the
cooling header set by the operating condition initial
setting unit 61. The hardness prediction unit 26
predicts the hardness distribution inside the rail 1
after the thermal treatment completion by using the
hardness prediction model 25 generated offline by using
the cooling condition data set generated online as input
data.
Further, in a case where the resetting of the
operating conditions is executed by the operating condition resetting unit 63, the hardness prediction unit
26 updates the initial setting of the operating
conditions, based on information after the resetting, and
predicts the hardness distribution inside the rail 1
after the thermal treatment completion again.
[0073]
<Operating condition determination unit 62>
The operating condition determination unit 62
compares the hardness distribution inside the rail 1
obtained by the hardness prediction unit 26 with the
target range of the hardness distribution inside the rail
1 received from the host computer 5.
Here, the target hardness inside the rail 1 can be
set so as to satisfy the hardness range defined in JIS
E1120 (2007), as illustrated in FIG. 10, for example.
Here, in JIS E1120, the upper and lower limit values of
the surface hardness of the head portion of the rail 1,
the upper limit value of the internal hardness, and the
lower limit value at a predetermined depth position (a
reference point) are defined.
Here, the position of the reference point is a
position at the distance of 11 mm from the surface.
[0074]
FIGS. 11A to 11C are diagrams illustrating examples
in a case where the hardness is out of the target
hardness range.
Here, it is a general feature that the internal
hardness of the head portion of the rail 1 decreases as the distance from the surface toward the inside increases. Therefore, as illustrated in FIGS. 12A and
12B, a target curve of the hardness distribution from the
surface layer of the rail 1 to a certain depth may be set
such that the difference from the hardness falls within a
certain range. At that time, the standard of JIS E1120
shall be satisfied within the allowable range of the
target curve of the hardness distribution.
[0075]
Further, the target hardness corresponding to the
hardness prediction position inside the rail 1 (the depth
from the surface is set to be di. i represents an
evaluation point (1 to n)) is set to be Bi, and whether
or not the following expression (1) is satisfied may be
determined by the allowable value a of the hardness error
set in advance, by using hardness BPi at each position
which is predicted. 2 Eni= 1 (Bi-BPi) < a ... (1)
In a case where the predicted internal hardness of
the rail 1 does not fall within the target hardness range
set in advance, a transition from the operating condition
determination unit 62 to the operating condition
resetting unit 63 is performed.
[0076]
<Operating condition resetting unit 63>
The operating condition resetting unit 63 resets the
cooling condition.
In the resetting of the cooling condition,
specifically, any of the injection flow rate, the
injection pressure, the injection distance, and the
switching timing of the cooling step of each cooling
header in each step from the start of cooling to the end
of cooling, or a plurality of operating parameters are
reset. The reset operating parameters are used in the
hardness prediction unit 26.
In this way, the correction of the operating
parameters is executed such that the predicted hardness
distribution inside the rail 1 falls within the target
hardness range.
Here, in the resetting of the cooling conditions, it
is necessary to predict the hardness distribution from
several times to several ten times. However, since the
learned model is generated offline in advance and the
hardness prediction is performed using the generated
learned model, it is possible to execute output of the
hardness prediction result with respect to one cooling
condition data set in a short time. That is, even if the
recalculation from several times to several ten times is
performed, it is possible to perform the resetting in a
short time as a whole.
[0077]
<Cooling control unit 64>
A cooling control unit 64 executes the forced
cooling treatment in the cooling facility 7 under the
operating conditions in which it is determined that the hardness distribution inside the rail 1 obtained by the hardness prediction unit 26 is in the target range.
That is, the cooling control unit 64 performs
control to execute the forced cooling at the switching
timing of the injection flow rate, the injection
pressure, the injection distance, and the cooling step of
each cooling header which is predicted to have a hardness
within the target range.
Here, there is a case where it takes several seconds
to open and close the valve of the cooling facility 7,
and a delay of several seconds occurs even in a case
where the injection distance is changed. Therefore, a
command to change the cooling conditions may be adjusted
in consideration of a response time required for a change
of the cooling conditions of each cooling header.
[0078]
Further, the setting of the operating conditions of
the cooling facility 7 can be carried out for each header
divided in the longitudinal direction of the rail 1. In
particular, since the speed at which the head and tail
ends of the rail 1 pass through the rolling machine
during rolling is not constant, the amount of cooling due
to the contact with a roll, roll cooling water, and
descaling water is increased, and the temperature easily
decreases compared to that of a steady part in the center
in the longitudinal direction. Therefore, the
temperature distribution in the longitudinal direction of
the rail 1 is measured by the thermometer 8 on the inlet side of the cooling facility 7, and the above method is applied to each position of the cooling header divided in the longitudinal direction to individually control the cooling conditions at each position in the longitudinal direction. In this way, even if the cooling start temperature is distributed in the longitudinal direction, it is possible to manufacture the rail 1 having uniform hardness in the longitudinal direction after the end of cooling.
[0079]
(Operation and others)
The execution of the internal hardness offline
calculation unit 22, which performs computing in advance
by a calculation formula based on a physical model, is
performed offline. In this way, in the present
embodiment, it is possible to accurately execute
processing of computing data (data for learning) of the
hardness distribution inside the rail 1 after the forced
cooling with respect to a plurality of cooling
conditions, which is processing with a large calculation
load using heat transfer analysis or the like.
Further, in the present embodiment, the hardness
prediction model 25 for obtaining data of the hardness
distribution inside the rail 1 after the forced cooling
with respect to the cooling conditions, based on a large
number of highly accurate data for learning, is obtained
by the machine learning.
Then, by executing the hardness prediction by the
hardness prediction model 25 online, it becomes possible
to output the hardness prediction result by the internal
hardness offline calculation unit 22 that performs a
complicated calculation at an extremely high speed.
[00801
The data for learning in the database 23 can be
created separately from the online operation of the
cooling facility 7. Therefore, it is possible to
accumulate the data set in the database 23 at any time,
and update the hardness prediction model 25 periodically
(for example, once a month). In this way, the number of
data sets that are the basis of the hardness prediction
model 25 increases, and the accuracy of the output result
of the learned model is improved. In particular, unlike
the data that is accumulated in actual operation, the
values of the cooling condition data set can be set
intentionally, and therefore, statistical bias does not
easily occur in the cooling condition data set, and the
data becomes suitable for the machine learning.
Therefore, there is a feature that the accuracy improves
as the number of data sets increases.
[0081]
In the present embodiment, since high-precision
hardness prediction can be executed online in a short
time as described above, the forced cooling (thermal
treatment) is executed under the operating conditions
with the hardness inside the rail 1 as the target hardness range. As a result, in the present embodiment, for example, it becomes possible to appropriately execute the microstructure control from the head portion surface to the inside of the heat hardened rail 1. As a result, it becomes possible to manufacture the heat hardened rail
1 in which variation in hardness of each rail 1 to be
manufactured or variation in hardness in the longitudinal
direction of the rail 1 is reduced and quality variation
is suppressed.
[0082]
(Effects)
(1) The present embodiment is the hardness
prediction method for a heat hardened rail 1, of
predicting, after the thermal treatment process in which
the rail 1 having a temperature equal to or higher than
the austenite region temperature is forcibly cooled in
the cooling facility 7, the hardness of the rail 1, the
method including: acquiring, by using the internal
hardness computing model that is a physical model for
performing computing by using a cooling condition data
set having at least a surface temperature of the rail 1
before the start of cooling and the operating conditions
of the cooling facility 7 for forced cooling as input
data and using the hardness inside at least the rail 1
head portion of the rail 1 after the forced cooling as
output data, a plurality of sets of data for learning
composed of the cooling condition data set and the
hardness output data; generating in advance the hardness prediction model 25 using the cooling condition data set as at least input data and using the hardness inside the rail 1 after the forced cooling as output data, by the machine learning using the acquired plurality of sets of data for learning; and predicting the hardness of the rail 1 after the thermal treatment process, based on the hardness inside the rail 1 with respect to a set of cooling condition data sets that are set as the cooling conditions of the thermal treatment process, obtained by using the hardness prediction model 25.
[0083]
For example, there is used the hardness prediction
device 20 for the heat hardened rail 1, which predicts,
after the thermal treatment process in which the rail 1
having a temperature equal to or higher than the
austenite region temperature is forcibly cooled in the
cooling facility 7, the hardness of the rail 1, the
device including: the database 23 that stores a plurality
of sets of data for learning computed using the internal
hardness computing model that is a physical model for
performing computing by using the cooling condition data
set having at least the surface temperature of the rail 1
before the start of cooling and the operating conditions
of the cooling facility 7 for forced cooling as input
data and the hardness inside at least the rail head
portion of the rail 1 after the forced cooling as output
data, and composed of the cooling condition data set and
the hardness output data; the hardness prediction model generation unit 24 that generates the hardness prediction model 25 using the cooling condition data set as at least input data and the hardness inside the rail 1 after the forced cooling as output data, by the machine learning using the plurality of sets of data for learning; and the hardness prediction unit 26 that predicts the hardness of the rail 1 after the thermal treatment process, based on the hardness inside the rail 1 with respect to a set of cooling condition data sets that are set as the cooling conditions of the thermal treatment process, by using the hardness prediction model 25.
[0084]
According to this configuration, since high
precision hardness prediction can be executed online in a
short time, forced cooling (thermal treatment) is
executed under the operating conditions with the hardness
inside the rail 1 as the target hardness range. As a
result, in the present embodiment, for example, it
becomes possible to appropriately control the
microstructure from the surface of the head portion to
the inside of the heat hardened rail 1, and it becomes
possible to manufacture the heat hardened rail 1 in which
variation in hardness of each rail 1 to be manufactured
or variation in hardness in the longitudinal direction of
the rail 1 is reduced and quality variation is
suppressed.
[0085]
(2) In the present embodiment, output data computed using the internal hardness computing model is a hardness distribution in at least a region from the surface of the rail 1 to a depth set in advance.
According to this configuration, it becomes possible
to more reliably predict the hardness for thermal
treatment.
[00861
(3) In the present embodiment, the internal hardness
computing model includes the heat transfer coefficient
calculation unit 22A that calculates the heat transfer
coefficient of the surface of the rail 1 during the
thermal treatment using the cooling facility 7, the heat
conduction calculation unit 22B that calculates a
temperature history inside the rail 1 by the thermal
treatment by using the heat transfer coefficient
calculated by the heat transfer coefficient calculation
unit 22A as a boundary condition, the microstructure
calculation unit 22C that predicts the microstructure
inside the rail 1 considering phase transformation, from
the temperature distribution inside the rail 1 based on
the temperature history calculation calculated by the
heat conduction calculation unit 22B, and the hardness
calculation unit 22D that calculates the hardness inside
the rail 1 from the microstructure distribution inside
the rail 1 based on the microstructure prediction inside
the rail 1 calculated by the microstructure calculation
unit 22C.
According to this configuration, it becomes possible
to more reliably predict the hardness for thermal
treatment.
[0087]
(4) The present embodiment is the thermal treatment
method for the heat hardened rail 1 having a thermal
treatment process in which the rail 1 having a
temperature equal to or higher than the austenite region
temperature is forcibly cooled in the cooling facility 7,
the method including: predicting the hardness inside the
rail 1 by the hardness prediction method for the heat
hardened rail 1 of the present embodiment, before the
start of cooling of the rail 1 in the cooling facility 7;
and resetting, in a case where the predicted hardness
inside the rail 1 is out of a target hardness range, the
operating conditions of the cooling facility 7 such that
the predicted hardness inside the rail 1 falls within the
target hardness range.
[0088]
For example, there is used the thermal treatment
device for the heat hardened rail 1 having a thermal
treatment process in which the rail 1 having a
temperature equal to or higher than the austenite region
temperature is forcibly cooled in the cooling facility 7,
the device including: the hardness prediction unit 26
that predicts the hardness inside the rail 1 by the
hardness prediction device 20 for the heat hardened rail
1 according to the present embodiment, before the start of cooling of the rail 1 in the cooling facility 7; and the operating condition resetting unit 63 that resets, in a case where the hardness inside the rail 1 predicted by the hardness prediction unit 26 is out of a target hardness range, the operating conditions of the cooling facility 7 such that the predicted hardness inside the rail 1 falls within the target hardness range.
[00891
According to this configuration, the forced cooling
(thermal treatment) can be executed under the operating
conditions in which the hardness inside the rail 1 is
within the target hardness range. As a result, in the
present embodiment, for example, it becomes possible to
appropriately control the microstructure from the surface
of the head portion to the inside of the heat hardened
rail 1, and it becomes possible to manufacture the heat
hardened rail 1 in which variation in hardness of each
rail 1 to be manufactured or variation in hardness in the
longitudinal direction of the rail 1 is reduced and
quality variation is suppressed.
[00901
(5) In the present embodiment, the operating
conditions of the cooling facility 7 to be reset include
at least one operating condition among the injection
pressure, the injection distance, the injection position,
and the injection time of a cooling medium that is
injected toward the rail 1 in the cooling facility 7.
According to this configuration, it becomes possible
to more reliably set the operating conditions in which
the hardness inside the rail 1 is the target hardness
range.
[0091]
(6) In the present embodiment, the cooling facility
7 has a plurality of cooling zones disposed along the
longitudinal direction of the rail 1 to be cooled, and
the resetting of the operating conditions of the cooling
facility 7 is executed individually for each of the
cooling zones.
According to this configuration, it becomes possible
to more finely set the operating conditions in which the
hardness inside the rail 1 is the target hardness range.
[0092]
(7) The present embodiment is the method for
generating the hardness prediction model 25 for
obtaining, after the rail 1 having a temperature equal to
or higher than the austenite region temperature is
forcibly cooled in the cooling facility 7, the hardness
of the rail 1 from the cooling condition data set having
at least the surface temperature of the rail 1 before the
start of cooling in the cooling facility 7 and the
operating conditions of the cooling facility 7 for the
forced cooling, the method including: acquiring, by using
the internal hardness computing model that is a physical
model for performing computing by using the cooling
condition data set as input data and using the hardness inside at least the rail head portion of the rail 1 after the forced cooling as output data, a plurality of sets of data for learning composed of the cooling condition data set and the hardness output data; and generating in advance the hardness prediction model 25 using the cooling condition data set as at least input data and using the hardness inside the rail 1 after the forced cooling as output data, by the machine learning using the acquired plurality of sets of data for learning.
[0093]
According to this configuration, it becomes possible
to generate the hardness prediction model 25 capable of
executing high-precision hardness prediction online in a
short time.
The hardness prediction model 25 may be, for
example, a neural network model (including a deep
learning model), a random forest, or a model learned by
SVM regression.
[0094]
(8) In the present embodiment, the output data that
is computed using the internal hardness computing model
is a hardness distribution in at least a region from the
surface of the rail 1 to a depth set in advance, and the
output data of the hardness prediction model 25 is also
data of the hardness distribution in at least a region
from the surface of the rail 1 to a depth set in advance.
According to this configuration, it becomes possible
to obtain, with high accuracy, information for
manufacturing a rail in which at least the surface layer
of the rail head portion has excellent hardness
uniformity.
[00951
(9) In the present embodiment, there is provided the
method for manufacturing the heat hardened rail 1
including the thermal treatment method for the heat
hardened rail 1 of the embodiment. For example, there is
provided the manufacturing facility for the heat hardened
rail 1 having the thermal treatment device for the heat
hardened rail 1 of the embodiment.
According to this configuration, it becomes possible
to manufacture a rail in which at least the surface layer
of the rail head portion has excellent hardness
uniformity.
Example
[00961
Next, examples based on the present embodiment will
be described.
The heat hardened rail 1 was manufactured by using
the manufacturing facility 2 for the rail 1 (refer to
FIG. 1).
In the present example, the rails 1 of a plurality
of rail types and standards were forcibly cooled, and
after air cooling to room temperature, the microstructure
of the head portion and the hardness distribution in the cross section were evaluated. In each example and each comparative example, 20 pieces of heat hardened rails 1 were manufactured, and variation in each rail was evaluated.
The target rails 1 were set to be a total of four
types, two types of rails (JIS 60 kg rail and 50 kg N
rail) and two types of standards (high hardness rail H
and normal hardness rail L). Then, after hot rolling was
completed at about 9000C, forced cooling was performed by
the cooling facility 7 installed online while keeping a
rolling length (without cutting). The austenite
temperature of the steel grade used in the present
example was 7600C, and the equilibrium transformation
temperature was 720°C.
[0097]
A target value of the inlet-side temperature by the
inlet-side thermometer 8 of the cooling facility 7 was
set to 7500C, and the cooling condition set in advance by
offline calculation was set as an indicated value of the
operating condition initial setting unit 61 such that
target hardness distributions were obtained with respect
to the four types of rails 1.
As the cooling condition, cooling was performed by a
two-step method, and the setting values of the injection
pressure in the front stage step and the subsequent stage
step and a switching time from the front stage step to
the subsequent stage step were set according to the type of the rail 1 to be thermally treated (in the table,
"fixed" indicates a standard condition).
[00981
In the present example, the used hardness prediction
model 25 corresponded to the four types of rails 1 of
Examples 1 to 4, and the hardness prediction model 25
corresponding to each rail was generated. In the
database 23 used to generate the hardness prediction
model 25, the relationship between the microstructure and
the hardness was created by a regression formula by
experiments using a one-stage cooling method in which the
injection flow rate and pressure of the cooling nozzle
are variously changed by using a laboratory scale cooling
experimental device. The number of data used to generate
the hardness prediction model 25 was 500.
At this time, in the actual operation, between the
temperature value measured by the inlet-side thermometer
8 and the target temperature, variation in each of the 20
rails 1 and temperature variation according to a position
in one rail were combined, resulting in variation in a
range of -30 to +10°C.
[00991
In the comparative examples, even if there are these
variations, a fixed pattern set by the operating
condition initial setting unit 61 was used as the cooling
condition. On the other hand, in the present example,
the actual measurement value by the inlet-side
thermometer and the cooling condition set in advance by the operating condition initial setting unit 61 were used as the cooling condition data set, the hardness distribution inside the rail 1 was predicted, and it was determined whether or not it was within the target hardness range. Here, with respect to the target hardness, as the target hardness illustrated in FIG. 10, the "lower limit" and the "upper limit" of the hardness of the surface were defined in Table 1 and defined by the
"lower limit" at the "reference point" and the "upper
limit" of the "whole" of the inside.
[0100]
In the present example, in a case where the hardness
distribution inside the rail 1 predicted by the hardness
prediction unit 26 fell within the target hardness range,
the forced cooling was performed under the initially set
cooling conditions, and in a case where the hardness
distribution deviated from the target range, the
injection pressure in the front stage step, the injection
pressure in the subsequent stage step (adjusted within
the range of the "injection pressure adjustment amount"
in the table), and the timing of transition from the
front stage step to the subsequent stage step (the
corrected range of "injection time adjustment amount" in
the table) were changed. In the present example, the
injection distance was set to be constant (15 mm) during
cooling regardless of the type of the rail in both the
examples and the comparative examples.
[0101]
After the end of the cooling, the rail 1 was removed
from the restraining device, transported to the cooling
bed 10, and air-cooled to room temperature. Then, the
rail 1 air-cooled to room temperature was cut, and the
microstructure observation of the head portion and the
hardness test were performed. For the hardness
measurement and microstructure observation of the head
portion, the rail 1 having a total length of 100 m was
divided into five pieces, and samples were taken at each
position (for each condition, 20 pieces of the rails 1 x
5 sample = 100). The head portion microstructure was
evaluated by observing the cut surface of the sample with
an SEM (scanning electron microscope). Further, the
hardness was evaluated by a Brinell hardness test at each
depth position in a range of 0 to 20 mm from the head top
surface. With respect to the measurement results of the
hardness, the maximum value and the minimum value in 100
pieces of data were evaluated.
[0102]
The "maximum" and "minimum" of the "reference point"
in Table 1 and the "maximum" and "minimum" in the surface
correspond to this. A case where these values were
within the range of the upper and lower limit values of
the target hardness and within the range of the "upper
limit" in the whole of the inside was defined as "0".
Further, as a result of microstructure observation, an
abnormal microstructure such as the bainitic structure or
the martensite structure did not occur in all the examples, and those having the pearlite structure were evaluated as "o". The experimental conditions and the evaluation results are shown in Table 1.
o 'a a
. 20
a00
0 0
E-2
CD2 CD0
[01041
As can be seen from Table 1, in Examples 1 to 4, the
hardness variation of the rail 1 was reduced, an abnormal
microstructure such as the bainitic structure or the
martensite structure did not occur in all the examples,
and the uniform rail 1 could be stably manufactured.
On the other hand, in the comparative examples, the
thermal treatment was appropriately performed under the
condition that the inlet-side temperature of the cooling
facility 7 was close to the target temperature, and the
target hardness and microstructure were obtained.
However, in a case of being deviated from the target
temperature, variation in hardness was large, and in some
cases, the formation of an abnormal microstructure was
observed.
[0105]
Here, the entire contents of Japanese Patent
Application No. 2020-100895 (filed on June 10, 2020),
from which the present application claims priority, form
part of the present disclosure by reference. Here, the
description has been made with reference to a limited
number of embodiments. However, the scope of rights is
not limited thereto, and modifications of each embodiment
based on the above disclosure will be obvious to those
skilled in the art.
Reference Signs List
[0106]
1 heat hardened rail
2 manufacturing facility
3 rolling machine
4 cutting machine
5 host computer
6 control device
7 cooling facility
8 thermometer
10 cooling bed
11 heating furnace
20 hardness prediction device
21 basic data acquisition unit
22 internal hardness offline calculation unit
22A heat transfer coefficient calculation unit
22B heat conduction calculation unit
22C microstructure calculation unit
22D hardness calculation unit
23 database
24 hardness prediction model generation unit
25 hardness prediction model
26 hardness prediction unit
61 operating condition initial setting unit
62 operating condition determination unit
63 operating condition resetting unit
64 cooling control unit
71 head top cooling header
72 head side cooling header
73 foot underside cooling header
74 head portion thermometer
75 foot portion thermometer
[0106]
The reference in this specification to any prior
publication (or information derived from it), or to any
matter which is known, is not, and should not be taken as
an acknowledgment or admission or any form of suggestion
that that prior publication (or information derived from
it) or known matter forms part of the common general
knowledge in the field of endeavour to which this
specification relates.
[0107]
Throughout this specification and the claims which
follow, unless the context requires otherwise, the word
"comprise", and variations such as "comprises" and
"comprising", will be understood to imply the inclusion
of a stated integer or step or group of integers or steps
but not the exclusion of any other integer or step or
group of integers or steps.
Claims (16)
1. A hardness prediction method for a heat
hardened rail, of predicting, after a thermal treatment
process in which a rail having a temperature equal to or
higher than an austenite region temperature is forcibly
cooled in a cooling facility, hardness of the rail, the
method comprising:
acquiring, by using an internal hardness computing
model that is a physical model for performing computing
by using a cooling condition data set having at least a
surface temperature of the rail before a start of cooling
and operating conditions of the cooling facility for the
forced cooling as input data and using hardness inside at
least a rail head portion of the rail after the forced
cooling as output data, a plurality of sets of data for
learning composed of the cooling condition data set and
the hardness output data;
generating in advance a hardness prediction model
using the cooling condition data set as at least input
data and using information on hardness inside the rail
after the forced cooling as output data, by machine
learning using the acquired plurality of sets of data for
learning; and
predicting the hardness of the rail after the
thermal treatment process, based on information on the
hardness inside the rail with respect to a set of cooling
condition data sets set as cooling conditions of the thermal treatment process for achieving target hardness, obtained by using the hardness prediction model.
2. The hardness prediction method for a heat
hardened rail according to claim 1,
wherein output data computed using the internal
hardness computing model is a hardness distribution in at
least a region from a rail surface to a depth set in
advance.
3. The hardness prediction method for a heat
hardened rail according to claim 1 or claim 2,
wherein the internal hardness computing model
includes
a heat transfer coefficient calculation unit
configured to calculate a heat transfer coefficient of a
rail surface during thermal treatment using the cooling
facility,
a heat conduction calculation unit configured to
calculate a temperature history inside the rail by the
thermal treatment by using the heat transfer coefficient
calculated by the heat transfer coefficient calculation
unit as a boundary condition,
a microstructure calculation unit configured to
predict a microstructure inside the rail considering
phase transformation, from the temperature distribution
inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, and a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit.
4. A thermal treatment method for a heat
hardened rail having a thermal treatment process in which
a rail having a temperature equal to or higher than an
austenite region temperature is forcibly cooled in a
cooling facility, the method comprising:
measuring a surface temperature of the rail before
a start of cooling;
predicting hardness inside the rail by using the
measured surface temperature of the rail by the hardness
prediction method for a heat hardened rail according to
any one of claims 1 to claim 3, before the start of
cooling of the rail in the cooling facility; and
resetting, in a case where the predicted hardness
inside the rail is out of a target hardness range,
operating conditions of the cooling facility such that
the predicted hardness inside the rail falls within the
target hardness range.
5. The thermal treatment method for a heat
hardened rail according to claim 4, wherein the operating conditions of the cooling facility to be reset include at least one operating condition among an injection pressure, an injection distance, an injection position, and an injection time of a cooling medium injected toward the rail in the cooling facility.
6. The thermal treatment method for a heat
hardened rail according to claim 4 or claim 5,
wherein the cooling facility has a plurality of
cooling zones disposed along a longitudinal direction of
the rail to be cooled, and
the resetting of the operating conditions of the
cooling facility is executed individually for each of the
cooling zones.
7. A method for generating a hardness prediction
model for obtaining, after a rail having a temperature
equal to or higher than an austenite region temperature
is forcibly cooled in a cooling facility, hardness of the
rail from a cooling condition data set for achieving
target hardness having at least a surface temperature of
the rail before a start of cooling in the cooling
facility and operating conditions of the cooling facility
for the forced cooling, the method comprising:
acquiring, by using an internal hardness computing
model that is a physical model for performing computing
by using the cooling condition data set as input data and using hardness inside at least a rail head portion of the rail after the forced cooling as output data, a plurality of sets of data for learning composed of the cooling condition data set and the hardness output data; and generating in advance a hardness prediction model using the cooling condition data set as at least input data and using information on hardness inside the rail after the forced cooling as output data, by machine learning using the acquired plurality of sets of data for learning.
8. The method for generating a hardness
prediction model according to claim 7,
wherein output data computed using the internal
hardness computing model is a hardness distribution in at
least a region from a rail surface to a depth set in
advance.
9. The method for generating a hardness
prediction model according to claim 7 or claim 8,
wherein the hardness prediction model is a neural
network model, a random forest, or a model learned by SVM
regression.
10. A method for manufacturing a heat hardened
rail comprising:
the thermal treatment method for a heat hardened
rail according to any one of claims 4 to claim 6.
11. A hardness prediction device for a heat
hardened rail, which predicts, after a thermal treatment
process in which a rail having a temperature equal to or
higher than an austenite region temperature is forcibly
cooled in a cooling facility, hardness of the rail, the
device comprising:
a database configured to store a plurality of sets
of data for learning computed using an internal hardness
computing model that is a physical model for performing
computing by using a cooling condition data set having at
least a surface temperature of the rail before a start of
cooling and operating conditions of the cooling facility
for the forced cooling as input data and using hardness
inside at least a rail head portion of the rail after the
forced cooling as output data, and composed of the
cooling condition data set and the hardness output data;
a hardness prediction model generation unit
configured to generate a hardness prediction model using
the cooling condition data set as at least input data and
using information on hardness inside the rail after the
forced cooling as output data, by machine learning using
the plurality of sets of data for learning;
a thermometer configured to measure the surface
temperature of the rail before the start of cooling; and
a hardness prediction unit configured to predict the
hardness of the rail after the thermal treatment process,
based on information on the hardness inside the rail with respect to a set of cooling condition data sets set as cooling conditions of the thermal treatment process for achieving target hardness, by using a measured value measured by the thermometer and the hardness prediction model.
12. The hardness prediction device for a heat
hardened rail according to claim 11,
wherein output data computed using the internal
hardness computing model is a hardness distribution in at
least a region from a rail surface to a depth set in
advance.
13. The hardness prediction device for a heat
hardened rail according to claim 11 or claim 12,
wherein the internal hardness computing model
includes
a heat transfer coefficient calculation unit
configured to calculate a heat transfer coefficient of
the rail surface during thermal treatment using the
cooling facility,
a heat conduction calculation unit configured to
calculate a temperature history inside the rail by the
thermal treatment by using the heat transfer coefficient
calculated by the heat transfer coefficient calculation
unit as a boundary condition,
a microstructure calculation unit configured to
predict a microstructure inside the rail considering phase transformation, from the temperature distribution inside the rail based on the temperature history calculation calculated by the heat conduction calculation unit, and a hardness calculation unit configured to calculate the hardness inside the rail from a microstructure distribution inside the rail based on the microstructure prediction inside the rail calculated by the microstructure calculation unit.
14. A thermal treatment device for a heat
hardened rail having a thermal treatment process in which
a rail having a temperature equal to or higher than an
austenite region temperature is forcibly cooled in a
cooling facility, the device comprising:
a hardness prediction unit configured to predict
hardness inside the rail by the hardness prediction
device for a heat hardened rail according to any one of
claims 11 to claim 13, before a start of cooling of the
rail in the cooling facility; and
an operating condition resetting unit configured to
reset, in a case where the hardness inside the rail
predicted by the hardness prediction unit is out of a
target hardness range, operating conditions of the
cooling facility such that the predicted hardness inside
the rail falls within the target hardness range.
15. The thermal treatment device for a heat
hardened rail according to claim 14,
wherein the operating conditions of the cooling
facility to be reset include at least one operating
condition among an injection pressure, an injection
distance, an injection position, and an injection time of
a cooling medium injected toward the rail in the cooling
facility.
16. A manufacturing facility for a heat hardened
rail comprising:
the thermal treatment device for a heat hardened
rail according to claim 14 or claim 15.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020100895A JP7294243B2 (en) | 2020-06-10 | 2020-06-10 | HARDNESS PREDICTION METHOD FOR HEAT-TREATED RAIL, HEAT TREATMENT METHOD, HARDNESS PREDICTION DEVICE, HEAT TREATMENT APPARATUS, MANUFACTURING METHOD, MANUFACTURING EQUIPMENT, AND HARDNESS PREDICTION MODEL GENERATION METHOD |
| JP2020-100895 | 2020-06-10 | ||
| PCT/JP2021/009060 WO2021250957A1 (en) | 2020-06-10 | 2021-03-08 | Hardness prediction method of thermally-treated rail, thermal treatment method, hardness prediction device, thermal treatment device, manufacturing method, manufacturing facilities, and generating method of hardness prediction model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| AU2021288876A1 AU2021288876A1 (en) | 2023-01-19 |
| AU2021288876B2 true AU2021288876B2 (en) | 2024-01-04 |
Family
ID=78845487
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021288876A Active AU2021288876B2 (en) | 2020-06-10 | 2021-03-08 | Hardness prediction method of heat hardened rail, thermal treatment method, hardness prediction device, thermal treatment device, manufacturing method, manufacturing facilities, and generating method of hardness prediction model |
Country Status (9)
| Country | Link |
|---|---|
| US (1) | US20230221231A1 (en) |
| EP (1) | EP4166682A4 (en) |
| JP (1) | JP7294243B2 (en) |
| KR (1) | KR20230005985A (en) |
| CN (1) | CN115917288A (en) |
| AU (1) | AU2021288876B2 (en) |
| BR (1) | BR112022025159A2 (en) |
| CA (1) | CA3186874A1 (en) |
| WO (1) | WO2021250957A1 (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015523467A (en) * | 2012-06-11 | 2015-08-13 | シーメンス ソシエタ ペル アツィオーニＳｉｅｍｅｎｓ Ｓ．ｐ．Ａ． | Method and system for rail heat treatment |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE1492461A1 (en) | 1964-11-30 | 1970-02-12 | American Sterilizer Co | Sterilization process |
| JPS58140029A (en) | 1982-02-15 | 1983-08-19 | Toyo Soda Mfg Co Ltd | Oxychlorination method of ethylene |
| JPS61149436A (en) | 1984-12-24 | 1986-07-08 | Nippon Steel Corp | Heat treatment of rail |
| JP3945545B2 (en) * | 1996-02-27 | 2007-07-18 | Ｊｆｅスチール株式会社 | Rail heat treatment method |
| JP2005315703A (en) | 2004-04-28 | 2005-11-10 | Nippon Steel Corp | Method for predicting material in steel material |
| DE102012020844A1 (en) * | 2012-10-24 | 2014-04-24 | Thyssenkrupp Gft Gleistechnik Gmbh | Process for the thermomechanical treatment of hot-rolled profiles |
| AU2014245505B2 (en) | 2013-03-28 | 2016-09-15 | Jfe Steel Corporation | Rail manufacturing method and manufacturing equipment |
| JP7344550B2 (en) | 2018-12-19 | 2023-09-14 | ユニチカ株式会社 | Method for producing iron-nickel nanowires |
-
2020
- 2020-06-10 JP JP2020100895A patent/JP7294243B2/en active Active
-
2021
- 2021-03-08 CA CA3186874A patent/CA3186874A1/en active Pending
- 2021-03-08 AU AU2021288876A patent/AU2021288876B2/en active Active
- 2021-03-08 KR KR1020227042338A patent/KR20230005985A/en unknown
- 2021-03-08 BR BR112022025159A patent/BR112022025159A2/en unknown
- 2021-03-08 CN CN202180041784.0A patent/CN115917288A/en active Pending
- 2021-03-08 EP EP21820925.2A patent/EP4166682A4/en active Pending
- 2021-03-08 WO PCT/JP2021/009060 patent/WO2021250957A1/en unknown
- 2021-03-08 US US18/009,523 patent/US20230221231A1/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015523467A (en) * | 2012-06-11 | 2015-08-13 | シーメンス ソシエタ ペル アツィオーニＳｉｅｍｅｎｓ Ｓ．ｐ．Ａ． | Method and system for rail heat treatment |
Also Published As
| Publication number | Publication date |
|---|---|
| KR20230005985A (en) | 2023-01-10 |
| EP4166682A4 (en) | 2023-11-29 |
| AU2021288876A1 (en) | 2023-01-19 |
| JP7294243B2 (en) | 2023-06-20 |
| US20230221231A1 (en) | 2023-07-13 |
| WO2021250957A1 (en) | 2021-12-16 |
| EP4166682A1 (en) | 2023-04-19 |
| CA3186874A1 (en) | 2021-12-16 |
| BR112022025159A2 (en) | 2022-12-27 |
| JP2021195577A (en) | 2021-12-27 |
| CN115917288A (en) | 2023-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101889668B1 (en) | Rolling simulation device | |
| RU2404000C2 (en) | Method of cooling control, cooling control device and cooling water amount calculator | |
| KR101903298B1 (en) | Secondary cooling control method for continuous casting machine and secondary cooling control device | |
| KR101516476B1 (en) | Apparatus for calculating set value, method of calculating set value, and program recording medium for calculating set value | |
| CN103611734A (en) | Laminar cooling temperature control method and system | |
| JP5776642B2 (en) | Secondary cooling method and secondary cooling device for continuous casting machine | |
| JP6939637B2 (en) | Secondary cooling control device for continuous casting machine, secondary cooling control method for continuous casting machine, and program | |
| AU2021288876B2 (en) | Hardness prediction method of heat hardened rail, thermal treatment method, hardness prediction device, thermal treatment device, manufacturing method, manufacturing facilities, and generating method of hardness prediction model | |
| JP6558060B2 (en) | Thick steel plate cooling control method, cooling control device, manufacturing method, and manufacturing device | |
| JP2014140862A (en) | Secondary cooling control method and secondary cooling control device of continuous casting machine | |
| JP2012011448A (en) | Cooling control method of rolled material, and continuous rolling mill to which the cooling control method is applied | |
| JP7298645B2 (en) | Method for generating cross-sectional dimension variation prediction model for shaped steel, apparatus for generating cross-sectional dimension variation prediction model for shaped steel, method for predicting cross-sectional dimension of shaped steel, method for controlling cross-sectional dimension of shaped steel, and method for manufacturing shaped steel | |
| JP7287416B2 (en) | Thick steel plate manufacturing specification determination support device, manufacturing specification search method, computer program, computer-readable recording medium, and thick steel plate manufacturing method | |
| JP2012000663A (en) | Cooling control method for rolled material, and continuous rolling mill with the cooling control method applied | |
| JP2016209911A (en) | Method for estimating parameter constituting heat transfer rate model | |
| WO2021229949A1 (en) | Method for predicting temperature deviation in thick steel plate, method for controlling temperature deviation in thick steel plate, method for generating temperature deviation prediction model for thick steel plate, method for producing thick steel plate, and equipment for producing thick steel plate | |
| JP4714061B2 (en) | Rolling material cooling device | |
| JP2012171001A (en) | Steel strip cooling control method, steel strip cooling control device, and steel strip control cooling program | |
| KR101281317B1 (en) | Cooling control method and system for high carbon hot rolled steel sheet having low deviation in mechanical properties | |
| JP5803138B2 (en) | Crystal grain size prediction method, crystal grain size prediction device, and crystal grain size prediction program | |
| JP7452696B2 (en) | Manufacturing method and manufacturing equipment for thick steel plates | |
| WO2022209320A1 (en) | Method of generating steel-plate material prediction model, material prediction method, manufacturing method, and manufacturing facility | |
| WO2024062766A1 (en) | Method of predicting form of warping in metal strip, method of controlling form of warping in metal strip, method of manufacturing metal strip, method of generating warping-form prediction model, and device for controlling form of warping in metal strip | |
| EP4275806A1 (en) | Method for predicting shape of steel sheet, shape control method, manufacturing method, method for generating shape prediction model, and manufacturing equipment | |
| Mestrom et al. | Optimized & adaptable cooling path control through a novel runout table controller at hsm2 of tata steel in ijmuiden |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| DA3 | Amendments made section 104 |
Free format text: THE NATURE OF THE AMENDMENT IS: AMEND THE INVENTION TITLE TO READ HARDNESS PREDICTION METHOD OF HEAT HARDENED RAIL, THERMAL TREATMENT METHOD, HARDNESS PREDICTION DEVICE, THERMAL TREATMENT DEVICE, MANUFACTURING METHOD, MANUFACTURING FACILITIES, AND GENERATING METHOD OF HARDNESS PREDICTION MODEL |
|
| FGA | Letters patent sealed or granted (standard patent) |